Asymptotic expansions of posterior expectations, distributions and densities for stochastic processes

DOI10.1007/BF00052346zbMath0672.62029OpenAlexW1994394568MaRDI QIDQ1120212

Martin Crowder

Publication date: 1988

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf00052346

zbMATH Keywords

distribution functionsdensity functionsasymptotic posterior normalityAsymptotic expansionsBayesian posterior expectations

Mathematics Subject Classification ID

Asymptotic distribution theory in statistics (62E20) Markov processes: estimation; hidden Markov models (62M05)

Related Items

Bayesian Analysis of DSGE Models, Asymptotic expansion of the posterior density in high dimensional generalized linear models, Applications of Laplace’s method in Bayesian analysis and related topics, Laplace approximations and Bayesian information criteria in possibly misspecified models, Laplace approximations using \(n^\alpha\)-consistent estimators, Model selection in the presence of nonstationarity, An alternative quasi likelihood approach, Bayesian analysis and data-based inference for model specification

Cites Work

• Uniform asymptotic normality of the maximum likelihood estimator
• On Asymptotic Distributions of Estimates of Parameters of Stochastic Difference Equations
• An Asymptotic Expansion for Posterior Distributions
• Asymptotic Expansions Associated with Posterior Distributions
• The Bernstein-Von Mises Theorem for Markov Processes
• Note on the Consistency of the Maximum Likelihood Estimate
• On Wald's Proof of the Consistency of the Maximum Likelihood Estimate
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